This is collection of all Ml resources used in the udemy course.
The link of Udemy course is https://www.udemy.com/machinelearning/
Inline Link
Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.[2]
The name machine learning was coined in 1959 by Arthur Samuel.[1] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data[3] – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,[4]:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision.
Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining,[5] where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.[6][7]
Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.[8] Contents
1 Overview
1.1 Machine learning tasks
1.2 Machine learning applications
2 History and relationships to other fields
2.1 Relation to statistics
3 Theory
4 Approaches
4.1 Decision tree learning
4.2 Association rule learning
4.3 Artificial neural networks
4.3.1 Deep learning
4.4 Inductive logic programming
4.5 Support vector machines
4.6 Clustering
4.7 Bayesian networks
4.8 Reinforcement learning
4.9 Representation learning
4.10 Similarity and metric learning
4.11 Sparse dictionary learning
4.12 Genetic algorithms
4.13 Rule-based machine learning
4.13.1 Learning classifier systems
5 Applications
6 Limitations
7 Model assessments
8 Ethics
9 Software
9.1 Free and open-source software
9.2 Proprietary software with free and open-source editions
9.3 Proprietary software
10 Journals
11 Conferences
12 See also
13 References
14 Further reading
15 External links
Overview
Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E."[9] This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".[10] In Turing's proposal the various characteristics that could be possessed by a thinking machine and the various implications in constructing one are exposed. Machine learning tasks
Machine learning tasks are typically classified into two broad categories, depending on whether there is a learning "signal" or "feedback" available to a learning system:
Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. As special cases, the input signal can be only partially available, or restricted to special feedback:
Semi-supervised learning: the computer is given only an incomplete training signal: a training set with some (often many) of the target outputs missing.
Active learning: the computer can only obtain training labels for a limited set of instances (based on a budget), and also has to optimize its choice of objects to acquire labels for. When used interactively, these can be presented to the user for labeling.
Reinforcement learning: training data (in form of rewards and punishments) is given only as feedback to the program's actions in a dynamic environment, such as driving a vehicle or playing a game against an opponent.[4]:3
Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
ML is very popular.
Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.[2]
The name machine learning was coined in 1959 by Arthur Samuel.[1] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data[3] – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,[4]:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision.
Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining,[5] where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.[6][7]
Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.[8] Contents
1 Overview
1.1 Machine learning tasks
1.2 Machine learning applications
2 History and relationships to other fields
2.1 Relation to statistics
3 Theory
4 Approaches
4.1 Decision tree learning
4.2 Association rule learning
4.3 Artificial neural networks
4.3.1 Deep learning
4.4 Inductive logic programming
4.5 Support vector machines
4.6 Clustering
4.7 Bayesian networks
4.8 Reinforcement learning
4.9 Representation learning
4.10 Similarity and metric learning
4.11 Sparse dictionary learning
4.12 Genetic algorithms
4.13 Rule-based machine learning